As enterprise AI adoption accelerates, so too does the complexity of choosing the right foundation. Should companies invest in proprietary platforms like GPT-4 or Claude, or build on open-source models such as Meta’s Llama or Mistral? The answer increasingly lies not in technical specs alone, but in how each option aligns with an organization’s cost structure, data governance needs and long-term innovation strategy.
Recent research from McKinsey & Company underscores the growing momentum behind open systems: Over 50% of enterprises already report using open-source AI tools across their technology stack, and 76% expect to increase usage in the coming years. At the same time, proprietary platforms offer speed, reliability and white-glove scalability—often the shortest path to business impact. The trade-offs are real and consequential.
To help executive decision-makers navigate these choices, we turned to members of the Senior Executive AI Think Tank—a group of enterprise AI, machine learning and innovation leaders who are shaping the way organizations operationalize artificial intelligence. In the sections below, they break down the pros and cons of each approach and offer actionable guidance on when to build, when to buy and how to orchestrate the right AI model strategy for your organization’s evolving needs.
“The best leaders I’ve seen use both. They innovate where it matters and rent where it doesn’t.”
Leadership Leverage: It’s a Strategic Call
While the open versus proprietary model debate is often framed as a technical decision, Divya Parekh, Founder of The DP Group, argues it’s actually a leadership choice. “Proprietary models feel like scaling on autopilot,” she says. “Until you hit the ceiling and realize your differentiation lives in someone else’s ecosystem.” In contrast, open-source offers freedom and ownership, but also demands technical maturity, governance and the right talent to extract value.
Parekh encourages leaders to stop treating model selection as a binary decision. “The best leaders I’ve seen use both. They innovate where it matters and rent where it doesn’t,” she says. In today’s fast-moving AI landscape, the ability to mix and match models based on business priorities—not just tech specs—is where real leverage lies.
“The open versus closed debate is a false narrative. The real question is: What is the actual use case?”
Use Case First: One Size Doesn’t Fit All
Jim Liddle, Chief Innovation Officer of Data Intelligence and AI at Nasuni, urges executives to look beyond the open versus closed framing. “The real question is: What is the actual use case?” he says. Proprietary models like GPT-4 offer plug-and-play ease, rapid deployment and enterprise-ready performance, which is ideal for teams that want to move quickly without managing infrastructure.
Open-source, on the other hand, offers control over pricing, data privacy and customization—but comes with operational trade-offs. “Now you’re hiring ML ops talent, managing GPUs and explaining to your CFO why inference costs didn’t magically disappear—they just moved to your AWS bill,” Liddle notes. For leaders, the takeaway is to evaluate model selection based on ROI by use case—balancing convenience with control.
Modularity Over Monoliths
Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, highlights how open-source models like Llama and Mistral bring flexibility, transparency and cost savings, which is particularly valuable for enterprises looking to fine-tune models for specific domains or regulatory needs. However, he warns that many leaders underestimate the complexity: “They demand deep talent pools, custom infrastructure and disciplined governance capabilities.”
By contrast, proprietary models like GPT-4 and Claude accelerate adoption with scale, innovation and reliability—though often at the cost of flexibility and control. “True digital leaders will architect hybrid ecosystems,” Kashyap says. “The future of AI leadership lies in treating models as modular infrastructure, orchestrating the best of both worlds to balance innovation speed with operational resilience.”
Balancing Support, Flexibility and Talent
For Roman Vinogradov, VP of Product at Improvado, model choice is closely tied to internal capabilities. “Open models can significantly reduce costs since they often have no licensing fees,” he says. “However, you might face challenges in support and updates.” With open-source, you get flexibility—but also the responsibility of managing upkeep and reliability.
Meanwhile, proprietary models offer robust support, predictable performance and faster integration, which can be especially appealing for teams without deep AI expertise. “It’s easier to find experts on popular proprietary tools,” Vinogradov notes, while open-source may require niche hires. His advice: Consider your team’s strengths and long-term goals to ensure your AI stack is sustainable, not just affordable.
“Open‑source models have a high initial cost for hardware and talent, but become significantly more cost‑effective at scale.”
Cost and Control: The Long-Term Equation
Mohan Krishna Mannava, Data and AI Leader at Texas Health, lays out a clear cost-control-talent framework. “Proprietary platforms have a low upfront cost but high per-use fees that can escalate,” he explains. Open-source flips that dynamic: “It has a high initial cost for hardware and talent, but it’s significantly more cost-effective at scale.” Over time, open models may offer better economics—but only if your org can support them.
He also underscores the importance of transparency and data security. “Open-source is superior, offering full transparency and the ability to fine-tune with private, proprietary data,” he says. But success depends on talent. Proprietary platforms are more accessible to generalist developers, while open tools require experienced engineers. For Mannava, model choice boils down to one thing: strategic fit.
Flexibility and Friction: Pick Your Battles
Charles Yeomans, CEO and Founder of Atombeam, uses an apt metaphor: “Open-source models are like buying a sports car in kit form,” offering full control but requiring technical know-how and internal resources. “You better know your way around an engine and have a garage to work in,” he warns. In contrast, proprietary platforms are like leasing a luxury vehicle—fast, reliable and fully serviced, but costly and never truly yours.
Yeomans sees a hybrid approach as the smartest route. “Claude or GPT-4 can be used as a ‘Swiss Army knife’ for complex reasoning,” he says, while models like Llama or Mistral are ideal for high-volume use cases where “doing something ‘pretty good’ at scale beats doing it ‘perfect’ at premium prices.” The key is knowing where each model excels and deploying accordingly.
How Leaders Can Determine the Best Fit
- Build leadership clarity first. Recognize that open versus proprietary is a leadership decision, not merely a technical one.
- Match model to use case. Use proprietary platforms for rapid deployment and open models for domain‑specific differentiation.
- Model total cost over time. Include talent, operations, infrastructure and governance when evaluating open‑source versus proprietary.
- Assess talent readiness early. If niche ML‑ops talent is scarce, proprietary APIs provide a faster path.
- Treat AI models as infrastructure. Create a hybrid ecosystem that orchestras both open‑source and proprietary models.
- Plan for governance and scale. Ensure you have the internal maturity to scale open models or the vendor roadmap for proprietary platforms.
Designing a Hybrid AI Advantage
The evolving AI landscape means enterprise leaders must navigate not only which model to adopt, but how to deploy, govern and scale it to match their strategic ambitions. The insights from the Senior Executive AI Think Tank highlight that the optimal architecture often blends open‑source and proprietary models in a hybrid ecosystem—balancing cost, control, talent and scalability. Organizations that architect thoughtfully, match their capabilities to their use cases and treat models as composable infrastructure will be best positioned to extract sustained value from generative AI.
As generative AI continues maturing, the question won’t be “open or closed”—it will be, “How do we orchestrate the best of both for the business we want to build?” Senior executives who answer that question now will establish the platform from which innovation, differentiation and resilient AI operations can scale.
